intrusion detection

These datasets are used to detect Intrusions in Controller Area Network (CAN) bus. Intrusions are detected using various Machine Learning and Deep Learning algorithms.

.

114 views
  • Artificial Intelligence
  • Last Updated On: 
    Mon, 12/30/2019 - 00:26

    Collecting and analysing heterogeneous data sources from the Internet of Things (IoT) and Industrial IoT (IIoT) are essential for training and validating the fidelity of cybersecurity applications-based machine learning.  However, the analysis of those data sources is still a big challenge for reducing high dimensional space and selecting important features and observations from different data sources.

    215 views
  • Artificial Intelligence
  • Last Updated On: 
    Wed, 10/16/2019 - 03:10

    The proliferation of IoT systems, has seen them targeted by malicious third parties. To address this challenge, realistic protection and investigation countermeasures, such as network intrusion detection and network forensic systems, need to be effectively developed. For this purpose, a well-structured and representative dataset is paramount for training and validating the credibility of the systems. Although there are several network datasets, in most cases, not much information is given about the Botnet scenarios that were used.

    824 views
  • IoT
  • Last Updated On: 
    Wed, 10/16/2019 - 02:53

    One of the major research challenges in this field is the unavailability of a comprehensive network based data set which can reflect modern network traffic scenarios, vast varieties of low footprint intrusions and depth structured information about the network traffic. Evaluating network intrusion detection systems research efforts, KDD98, KDDCUP99 and NSLKDD benchmark data sets were generated a decade ago. However, numerous current studies showed that for the current network threat environment, these data sets do not inclusively reflect network traffic and modern low footprint attacks.

    328 views
  • Artificial Intelligence
  • Last Updated On: 
    Wed, 10/16/2019 - 02:31

    We created various types of network attacks in Internet of Things (IoT) environment for academic purpose. Two typical smart home devices -- SKT NUGU (NU 100) and EZVIZ Wi-Fi Camera (C2C Mini O Plus 1080P) -- were used. All devices, including some laptops or smart phones, were connected to the same wireless network. The dataset consists of 42 raw network packet files (pcap) at different time points.

    * The packet files are captured by using monitor mode of wireless network adapter. The wireless headers are removed by Aircrack-ng.

    2390 views
  • Security
  • Last Updated On: 
    Fri, 09/27/2019 - 04:57

    This work intend to identify characteristics in network traffic that are able to distinguish the normal network behavior from denial of service attacks. One way to classify anomalous traffic is the data analysis of the packets header. This dataset contains labeled examples of normal traffic (23.088 instances), TCP Flood attacks (14.988 instances), UDP Flood (6.894 instances), HTTP Flood (347 instances) and HTTP Slow (183 instances) distributed in 73 numeric variables.

    325 views
  • Security
  • Last Updated On: 
    Sun, 10/20/2019 - 15:48

    This dataset is benchmark dataset we use in our research for Intrusion Detection System.

    284 views
  • Security
  • Last Updated On: 
    Sat, 12/29/2018 - 18:44

    Efficient intrusion detection and analysis of the security landscape in big data environments present challenge for today's users. Intrusion behavior can be described by provenance graphs that record the dependency relationships between intrusion processes and the infected files. Existing intrusion detection methods typically analyze and identify the anomaly either in a single provenance path or the whole provenance graph, neither of which can achieve the benefit on both detection accuracy and detection time.

    56 views
  • Security
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34